A segmentation-based algorithm for classification of benign and malignancy Thyroid nodules with multi-feature information.

The aim of this study is to propose a new diagnostic model based on "segmentation + classification" to improve the routine screening of Thyroid nodule ultrasonography by utilizing the key domain knowledge of medical diagnostic tasks. A Multi-scale segmentation network based on a pyramidal pooling structure of multi-parallel void spaces is proposed. First, in the segmentation network, the exact information of the underlying feature space is obtained by an Attention Gate. Second, the inflated convolutional part of Atrous Spatial Pyramid Pooling (ASPP) is cascaded for multiple downsampling. Finally, a three-branch classification network combined with expert knowledge is designed, drawing on doctors' clinical diagnosis experience, to extract features from the original image of the nodule, the regional image of the nodule, and the edge image of the nodule, respectively, and to improve the classification accuracy of the model by utilizing the Coordinate attention (CA) mechanism and cross-level feature fusion. The Multi-scale segmentation network achieves 94.27%, 93.90% and 88.85% of mean precision (mPA), Dice value (Dice) and mean joint intersection (MIoU), respectively, and the accuracy, specificity and sensitivity of the classification network reaches 86.07%, 81.34% and 90.19%, respectively. Comparison tests show that this method outperforms the U-Net, AGU-Net and DeepLab V3+ classical models as well as the nnU-Net, Swin UNetr and MedFormer models that have emerged in recent years. This algorithm, as an auxiliary diagnostic tool, can help physicians more accurately assess the benign or malignant nature of Thyroid nodules. It can provide objective quantitative indicators, reduce the bias of subjective judgment, and improve the consistency and accuracy of diagnosis. Codes and models are available at https://github.com/enheliang/Thyroid-Segmentation-Network.git.

The ADnet Bayesian belief network for alder decline: Integrating empirical data and expert knowledge.

The globalization in plant material trading has caused the emergence of invasive pests in many ecosystems, such as the alder pathogen Phytophthora ×alni in European riparian forests. Due to the ecological importance of alder to the functioning of rivers and the increasing incidence of P. ×alni-induced alder decline, effective and accessible decision tools are required to help managers and stakeholders control the disease. This study proposes a Bayesian belief network methodology to integrate diverse information on the factors affecting the survival and infection ability of P. ×alni in riparian habitats to help predict and manage disease incidence. The resulting Alder Decline Network (ADnet) management tool integrates information about alder decline from scientific literature, expert knowledge and empirical data. Expert knowledge was gathered through elicitation techniques that included 19 experts from 12 institutions and 8 countries. An original dataset was created covering 1189 European locations, from which P. ×alni occurrence was modeled based on bioclimatic variables. ADnet uncertainty was evaluated through its sensitivity to changes in states and three scenario analyses. The ADnet tool indicated that mild temperatures and high precipitation are key factors favoring pathogen survival. Flood timing, water velocity, and soil type have the strongest influence on disease incidence. ADnet can support ecosystem management decisions and knowledge transfer to address P. ×alni-induced alder decline at local or regional levels across Europe. Management actions such as avoiding the planting of potentially infected trees or removing man-made structures that increase the flooding period in disease-affected sites could decrease the incidence of alder disease in riparian forests and limit its spread. The coverage of the ADnet tool can be expanded by updating data on the pathogen's occurrence, particularly from its distributional limits. Research on the role of genetic variability in alder susceptibility and pathogen virulence may also help improve future ADnet versions.

AI-powered microscopy image analysis for parasitology: integrating human expertise.

Microscopy image analysis plays a pivotal role in parasitology research. Deep learning (DL), a subset of artificial intelligence (AI), has garnered significant attention. However, traditional DL-based methods for general purposes are data-driven, often lacking explainability due to their black-box nature and sparse instructional resources. To address these challenges, this article presents a comprehensive review of recent advancements in knowledge-integrated DL models tailored for microscopy image analysis in parasitology. The massive amounts of human expert knowledge from parasitologists can enhance the accuracy and explainability of AI-driven decisions. It is expected that the adoption of knowledge-integrated DL models will open up a wide range of applications in the field of parasitology.

Invited Commentary: Where Do the Causal DAGs Come From?

How do we construct our causal DAGs, e.g. for life course modelling and analysis? In this commentary I review how the data-driven construction of causal DAGs (causal discovery) has evolved, what promises it holds and what limitations or caveats must be considered. In conclusion I find that expert- or theory-driven model building might benefit from some more checking against the data and causal discovery could bring new ideas into old theories.

Pest risk assessment of African Leucinodes species for the European Union.

Following a request from the European Commission, the EFSA Panel on Plant Health performed a quantitative risk assessment for the EU of African Leucinodes species (Lepidoptera: Crambidae), which are fruit and shoot borers, especially of eggplant type fruit. The assessment focused on (i) potential pathways for entry, (ii) distribution of infested imports within EU, (iii) climatic conditions favouring establishment, (iv) spread and (v) impact. Options for risk reduction are discussed, but their effectiveness was not quantified. Leucinodes spp. are widely distributed across sub-Saharan Africa but are little studied and they could be much more widespread in Africa than reported. Much African literature erroneously reports them as Leucinodes orbonalis which is restricted to Asia. The import of eggplant type fruit from sub-Saharan Africa consists of special fruit types and caters mostly to niche markets in the EU. The main pathway for entry is fruit of Solanum aethiopicum and exotic varieties of eggplant (S. melongena). CLIMEX modelling was used with two possible thresholds of ecoclimatic index (EI) to assess establishment potential. Climates favouring establishment occur mostly in southern Europe, where, based on human population, 14% of the imported produce is distributed across NUTS2 regions where EI ≥ 30; or where 23% of the produce is distributed where EI ≥ 15. Over the next 5 years, an annual median estimate of ~ 8600 fruits, originating from Africa, and infested with African Leucinodes spp. are expected to enter EU NUTS2 regions where EI ≥ 15 (90% CR ~ 570-52,700); this drops to ~ 5200 (90% CR ~ 350-32,100) in NUTS2 regions where EI ≥ 30. Escape of adult moths occurs mostly from consumer waste; considering uncertainties in pathway transfer, such as adult emergence, mate finding and survival of progeny, the annual median probability of a mated female establishing a founder population in NUTS regions where EI ≥ 15 was estimated to be 0.0078 (90% CR 0.00023-0.12125). This equates to a median estimate of one founder population ~ every 128 years (90% CR approximately one every 8-4280 years). Using an EI ≥ 30, the median number of founder populations establishing in the EU annually is 0.0048 (90% CR 0.0001-0.0739), equating to a median estimate of one founder population approximately every 210 years (90% CR approximately one every 14-7020 years). Under climate change for the period 2040-2059, the percent of infested produce going to suitable areas would be increased to 33% for EI ≥ 15 and to 21% for EI ≥ 30. Accordingly, the waiting time until the next founder population would be reduced to median estimates of 89 years for EI ≥ 15 (90% CR ~ 6-2980 years) and 139 years for EI ≥ 30 (90% CR 9-4655 years). If a founder population were to establish, it is estimated to spread at a rate of 0.65-7.0 km per year after a lag phase of 5-92 years. Leucinodes spp. are estimated to reduce eggplant yield by a median value of 4.5% (90% CR 0.67%-13%) if growers take no specific action, or 0.54% (90% CR between 0.13% and 1.9%) if they do take targeted action, matching previous estimates made during a risk assessment of L. orbonalis from Asia.

Pest risk assessment of Leucinodes orbonalis for the European Union.

Following a request from the European Commission, the EFSA Panel on Plant Health performed a quantitative risk assessment of Leucinodes orbonalis (Lepidoptera: Crambidae), the eggplant fruit and shoot borer, for the EU. The assessment focused on potential pathways for entry, climatic conditions favouring establishment, spread and impact. Options for risk reduction are discussed but effectiveness was not quantified. L. orbonalis is a key pest of eggplant (aubergine/brinjal) in the Indian subcontinent and occurs throughout most of southern Asia with records mostly from India and Bangladesh. The main pathway of entry is fruit of solanaceous plants, primarily exotic varieties of eggplant, Solanum melongena and turkey berry, S. torvum. The trade in both commodities from Asia is small but nevertheless dwarfs the trade in other Solanum fruits from Asia (S. aethiopicum, S. anguivi, S. virginianum, S. aculeatissimum, S. undatum). Other Solanum fruits were therefore not further assessed as potential pathways. The trade in eggplant from Asia consists of special fruit types and caters mostly to niche markets in the EU, while most eggplant consumed in Europe is produced in southern European and northern African countries, where L. orbonalis does not occur. Using expert knowledge elicitation (EKE) and pathway modelling, the Panel estimated that approximately 3-670 infested fruit (90% certainty range, CR) of S. melongena or fruit bunches of S. torvum enter into regions of the EU that are suitable for L. orbonalis establishment each year. Based on CLIMEX modelling, and using two possible thresholds of ecoclimatic index (EI) to indicate uncertainty in establishment potential, climates favouring establishment occur mostly in southern Europe, where, based on human population, approximately 14% of the imported produce is distributed across NUTS2 regions where EI ≥ 30; or 23% of the produce is distributed where EI ≥ 15. Escape of adult moths occurs mostly from consumer waste. By analysing results of different scenarios for the proportion of S. melongena and S. torvum in the trade, and considering uncertainties in the climatic suitability of southern Europe, adult moth emergence in areas suitable for establishment is expected to vary between 84 individuals per year and one individual per 40 years (based on 90% CR in different scenarios). In the baseline scenario, 25% of the solanaceous fruit from Asia is S. torvum, 75% is S. melongena and EI ≥ 30 is required for establishment. After accounting for the chances of mating, host finding and establishment, the probability of a mated female establishing a founder population in the EU is less than 1 in 100,000 to about 1 event per 622 years (90% CR in baseline scenario). The waiting time until the first establishment is then 622 to more than 100,000 years (CR). If such a founder population were established, the moth is estimated to spread at a rate of 0.65-7.0 km per year after a lag phase of 5-92 years. The impact of the insect on the production of eggplant is estimated to be 0.67%-13% (CR) if growers take no specific action against the insect and 0.13%-1.9% if they do take targeted actions. Tomato (S. lycopersicum) and potato (S. tuberosum) are hosts of L. orbonalis, but the insect does not develop to maturity in tomato fruit, and it does not feed on potato tubers under field conditions; hence, damage to potato can only occur due to feeding on shoots. Tomato and potato are not preferred hosts; nevertheless, impact can occur if populations of L. orbonalis are high and preferred hosts are not available. The Panel did not assess this damage due to insufficient information.

Traditional Chinese medicine diagnostic prediction model for holistic syndrome differentiation based on deep learning.

Background: With the development of traditional Chinese medicine (TCM) syndrome knowledge accumulation and artificial intelligence (AI), this study proposes a holistic TCM syndrome differentiation model for the classification prediction of multiple TCM syndromes based on deep learning and accelerates the construction of modern foundational TCM equipment. Methods: We searched publicly available TCM guidelines and textbooks for expert knowledge and validated these sources using ten-fold cross-validation. Based on the BERT and CNN models, with the classification constraints from TCM holistic syndrome differentiation, the TCM-BERT-CNN model was constructed, which completes the end-to-end TCM holistic syndrome text classification task through symptom input and syndrome output. We assessed the performance of the model using precision, recall, and F1 scores as evaluation metrics. Results: The TCM-BERT-CNN model had a higher precision (0.926), recall (0.9238), and F1 score (0.9247) than the BERT, TextCNN, LSTM RNN, and LSTM ATTENTION models and achieved superior results in model performance and predictive classification of most TCM syndromes. Symptom feature visualization demonstrated that the TCM-BERT-CNN model can effectively identify the correlation and characteristics of symptoms in different syndromes with a strong correlation, which conforms to the diagnostic characteristics of TCM syndromes. Conclusions: The TCM-BERT-CNN model proposed in this study is in accordance with the TCM diagnostic characteristics of holistic syndrome differentiation and can effectively complete diagnostic prediction tasks for various TCM syndromes. The results of this study provide new insights into the development of deep learning models for holistic syndrome differentiation in TCM.

Estimating the risk and benefit of radiation therapy in (y)pN1 stage breast cancer patients: A Bayesian network model incorporating expert knowledge (KROG 22-13).

BACKGROUND: We aimed to evaluate the risk and benefit of (y)pN1 breast cancer patients in a Bayesian network model. METHOD: We developed a Bayesian network (BN) model comprising three parts: pretreatment, intervention, and risk/benefit. The pretreatment part consisted of clinical information from a tertiary medical center. The intervention part regarded the field of radiotherapy. The risk/benefit component encompasses radiotherapy (RT)-related side effects and effectiveness, including factors such as recurrence, cardiac toxicity, lymphedema, and radiation pneumonitis. These factors were evaluated in terms of disability weights and probabilities from a nationwide expert survey. The overall disease burden (ODB) was calculated as the sum of the probability multiplied by the disability weight. A higher value of ODB indicates a greater disease burden for the patient. RESULTS: Among the 58 participants, a BN model utilizing discretization and clustering techniques revealed five distinct clusters. Overall, factors associated with breast reconstruction and RT exhibited high discrepancies (24-34 %), while RT-related side effects demonstrated low discrepancies (3-11 %) among the experts. When incorporating recurrence and RT-related side effects, the mean ODB of (y)pN1 patients was 0.258 (range, 0.244-0.337), with a higher tendency observed in triple-negative breast cancer (TNBC) or mastectomy cases. The ODB for TNBC patients undergoing mastectomy without postmastectomy radiotherapy was 0.327, whereas for non-TNBC patients undergoing breast conserving surgery with RT, the disease burden was 0.251. There was an increasing trend in ODB as the field of RT increased. CONCLUSION: We developed a Bayesian network model based on an expert survey, which helps to understand treatment patterns and enables precise estimations of RT-related risk and benefit in (y)pN1 patients.

Data-Driven Prediction and Uncertainty Quantification of Process Parameters for Directed Energy Deposition.

Laser-based directed energy deposition using metal powder (DED-LB/M) offers great potential for a flexible production mainly defined by software. To exploit this potential, knowledge of the process parameters required to achieve a specific track geometry is essential. Existing analytical, numerical, and machine-learning approaches, however, are not yet able to predict the process parameters in a satisfactory way. A trial-&-error approach is therefore usually applied to find the best process parameters. This paper presents a novel user-centric decision-making workflow, in which several combinations of process parameters that are most likely to yield the desired track geometry are proposed to the user. For this purpose, a Gaussian Process Regression (GPR) model, which has the advantage of including uncertainty quantification (UQ), was trained with experimental data to predict the geometry of single DED tracks based on the process parameters. The inherent UQ of the GPR together with the expert knowledge of the user can subsequently be leveraged for the inverse question of finding the best sets of process parameters by minimizing the expected squared deviation between target and actual track geometry. The GPR was trained and validated with a total of 379 cross sections of single tracks and the benefit of the workflow is demonstrated by two exemplary use cases.

Predicting odor profile of food from its chemical composition: Towards an approach based on artificial intelligence and flavorists expertise.

Odor is central to food quality. Still, a major challenge is to understand how the odorants present in a given food contribute to its specific odor profile, and how to predict this olfactory outcome from the chemical composition. In this proof-of-concept study, we seek to develop an integrative model that combines expert knowledge, fuzzy logic, and machine learning to predict the quantitative odor description of complex mixtures of odorants. The model output is the intensity of relevant odor sensory attributes calculated on the basis of the content in odor-active comounds. The core of the model is the mathematically formalized knowledge of four senior flavorists, which provided a set of optimized rules describing the sensory-relevant combinations of odor qualities the experts have in mind to elaborate the target odor sensory attributes. The model first queries analytical and sensory databases in order to standardize, homogenize, and quantitatively code the odor descriptors of the odorants. Then the standardized odor descriptors are translated into a limited number of odor qualities used by the experts thanks to an ontology. A third step consists of aggregating all the information in terms of odor qualities across all the odorants found in a given product. The final step is a set of knowledge-based fuzzy membership functions representing the flavorist expertise and ensuring the prediction of the intensity of the target odor sensory descriptors on the basis of the products' aggregated odor qualities; several methods of optimization of the fuzzy membership functions have been tested. Finally, the model was applied to predict the odor profile of 16 red wines from two grape varieties for which the content in odorants was available. The results showed that the model can predict the perceptual outcome of food odor with a certain level of accuracy, and may also provide insights into combinations of odorants not mentioned by the experts.

Expert knowledge-based peak current mode control of electrosurgical generators for improved output power regulation.

Electrosurgical generators (ESG) are widely used in medical procedures to cut and coagulate tissue. Accurate control of the output power is crucial for surgical success, but can be challenging to achieve. In this paper, a novel expert knowledge-based peak current mode controller (EK-PCMC) is proposed to regulate the output power of an ESG. The EK-PCMC leverages expert knowledge to adapt to changes in tissue impedance during surgical procedures. We compared the performance of the EK-PCMC with the classical peak current mode controller (PCMC) and fuzzy PID controller. The results demonstrate that the EK-PCMC significantly outperformed the PCMC, reducing the integral square error (ISE) and integral absolute error (IAE) by a factor of 3.88 and 4.86, respectively. In addition, the EK-PCMC outperformed the fuzzy PID controller in terms of transient response and steady-state performance. Our study highlights the effectiveness of the proposed EK-PCMC in improving the regulation of the output power of an ESG and improving surgical outcomes.

A homologous and heterogeneous multi-view inter-patient adaptive network for arrhythmia detection.

BACKGROUND AND OBJECTIVE: Electrocardiogram (ECG) is a widely used diagnostic tool for arrhythmia assessment in clinical practice. However, current arrhythmia detection algorithms rely heavily on signal-based data, while cardiologists often use image-based data. This discrepancy, combined with individual differences in physiological signals, poses challenges for accurate arrhythmia detection. To address these challenges and improve arrhythmia detection performance, we propose a homologous and heterogeneous multi-view inter-patient adaptive network. METHODS: We designed a multi-view representation learning module to capture dynamic and morphological characteristics from ECG signals and electrocardiographic images. Expert knowledge was also elicited to gain internally-invariant characteristics of each category. Finally, we designed a new loss function that aligns the embedding of the source and target domains in the feature space to minimize the negative effects of individual differences. RESULTS: Experiments on the MIT-BIH arrhythmia database demonstrate that our proposed method outperforms state-of-the-art baselines in terms of accuracy, positive predictive value, sensitivity and F1-score. These results indicate the effectiveness of our method in accurately detecting arrhythmias. CONCLUSIONS: Our homologous and heterogeneous multi-view inter-patient adaptive network successfully addresses the challenges of utilizing both ECG signal and electrocardiographic image data for arrhythmia detection and overcoming individual differences in physiological signals. Our proposed method has the potential to improve early diagnosis and treatment outcomes of arrhythmias in clinical practice.

Use of risk assessment and predictive microbiology in regulatory science related to the scientific opinions of the EFSA BIOHAZ Panel.

EFSA's Panel on Biological Hazards (BIOHAZ Panel) deals with questions on biological hazards relating to food safety and food-borne diseases. This covers food-borne zoonoses, transmissible spongiform encephalopathies, antimicrobial resistance, food microbiology, food hygiene, animal-by products, and associated waste management issues. The scientific assessments are diverse and frequently the development of new methodological approaches is required to deal with a mandate. Among the many risk factors, product characteristics (pH, water activity etc.), time and temperature of processing and storage along the food supply chain are highly relevant for assessing the biological risks. Therefore, predictive microbiology becomes an essential element of the assessments. Uncertainty analysis is incorporated in all BIOHAZ scientific assessments, to meet the general requirement for transparency. Assessments should clearly and unambiguously state what sources of uncertainty have been identified and their impact on the conclusions of the assessment. Four recent BIOHAZ Scientific Opinions are presented to illustrate the use of predictive modelling and quantitative microbial risk assessment principles in regulatory science. The Scientific Opinion on the guidance on date marking and related food information, gives a general overview on the use of predictive microbiology for shelf-life assessment. The Scientific Opinion on the efficacy and safety of high-pressure processing of food provides an example of inactivation modelling and compliance with performance criteria. The Scientific Opinion on the use of the so-called 'superchilling' technique for the transport of fresh fishery products illustrates the combination of heat transfer and microbial growth modelling. Finally, the Scientific Opinion on the delayed post-mortem inspection in ungulates, shows how variability and uncertainty, were quantitatively embedded in assessing the probability of Salmonella detection on carcasses, via stochastic modelling and expert knowledge elicitation.

Deterministic solution of algebraic equations in sentiment analysis.

Text mining methods usually use statistical information to solve text and language-independent procedures. Text mining methods such as polarity detection based on stochastic patterns and rules need many samples to train. On the other hand, deterministic and non-probabilistic methods are easy to solve and faster than other methods but are not efficient in NLP data. In this article, a fast and efficient deterministic method for solving the problems is proposed. In the proposed method firstly we transform text and labels into a set of equations. In the second step, a mathematical solution of ill-posed equations known as Tikhonov regularization was used as a deterministic and non-probabilistic way including additional assumptions, such as smoothness of solution to assign a weight that can reflect the semantic information of each sentimental word. We confirmed the efficiency of the proposed method in the SemEval-2013 competition, ESWC Database and Taboada database as three different cases. We observed improvement of our method over negative polarity due to our proposed mathematical step. Moreover, we demonstrated the effectiveness of our proposed method over the most common and traditional machine learning, stochastic and fuzzy methods.

Fusing Expert Knowledge with Monitoring Data for Condition Assessment of Railway Welds.

Monitoring information can facilitate the condition assessment of railway infrastructure, via delivery of data that is informative on condition. A primary instance of such data is found in Axle Box Accelerations (ABAs), which track the dynamic vehicle/track interaction. Such sensors have been installed on specialized monitoring trains, as well as on in-service On-Board Monitoring (OBM) vehicles across Europe, enabling a continuous assessment of railway track condition. However, ABA measurements come with uncertainties that stem from noise corrupt data and the non-linear rail-wheel contact dynamics, as well as variations in environmental and operational conditions. These uncertainties pose a challenge for the condition assessment of rail welds through existing assessment tools. In this work, we use expert feedback as a complementary information source, which allows the narrowing down of these uncertainties, and, ultimately, refines assessment. Over the past year, with the support of the Swiss Federal Railways (SBB), we have assembled a database of expert evaluations on the condition of rail weld samples that have been diagnosed as critical via ABA monitoring. In this work, we fuse features derived from the ABA data with expert feedback, in order to refine defection of faulty (defect) welds. Three models are employed to this end; Binary Classification and Random Forest (RF) models, as well as a Bayesian Logistic Regression (BLR) scheme. The RF and BLR models proved superior to the Binary Classification model, while the BLR model further delivered a probability of prediction, quantifying the confidence we might attribute to the assigned labels. We explain that the classification task necessarily suffers high uncertainty, which is a result of faulty ground truth labels, and explain the value of continuously tracking the weld condition.

Interpretable and Reliable Oral Cancer Classifier with Attention Mechanism and Expert Knowledge Embedding via Attention Map.

Convolutional neural networks have demonstrated excellent performance in oral cancer detection and classification. However, the end-to-end learning strategy makes CNNs hard to interpret, and it can be challenging to fully understand the decision-making procedure. Additionally, reliability is also a significant challenge for CNN based approaches. In this study, we proposed a neural network called the attention branch network (ABN), which combines the visual explanation and attention mechanisms to improve the recognition performance and interpret the decision-making simultaneously. We also embedded expert knowledge into the network by having human experts manually edit the attention maps for the attention mechanism. Our experiments have shown that ABN performs better than the original baseline network. By introducing the Squeeze-and-Excitation (SE) blocks to the network, the cross-validation accuracy increased further. Furthermore, we observed that some previously misclassified cases were correctly recognized after updating by manually editing the attention maps. The cross-validation accuracy increased from 0.846 to 0.875 with the ABN (Resnet18 as baseline), 0.877 with SE-ABN, and 0.903 after embedding expert knowledge. The proposed method provides an accurate, interpretable, and reliable oral cancer computer-aided diagnosis system through visual explanation, attention mechanisms, and expert knowledge embedding.

How expert insight into alpine peatland conservation complements global scientific evidence.

Experts can provide valuable information to fill knowledge gaps in published research on management effectiveness, particularly for threatened ecosystems, for which there is often limited evidence and the need for prompt intervention to ensure their persistence. One such ecosystem, alpine peatland, is threatened by climate change and other pressures, provides vital ecosystem services, and supports unique biodiversity. In a workshop, we gathered and synthesized into an accessible format information from experts on interventions used, threat context, and intervention effectiveness for Australian alpine peatland and used this knowledge to evaluate local relevance of the global literature for this threatened ecosystem. Experts identified 15 interventions used to conserve Australian peatlands, most of which enhanced or restored peatland condition and effectively addressed diverse threats. Experts' perspectives and global studies were strongly aligned, suggesting that research on peatland management may be broadly relevant across contexts, despite the distinct characteristics of Australian systems. Our workshop-based expert elicitation approach provided insights into current management practices unavailable in the literature.

Cómo los conocimientos de expertos sobre la conservación de las turberas alpinas complementan la evidencia científica mundial Resumen Los expertos pueden proporcionar información valiosa para llenar los vacíos de conocimiento en las investigaciones publicadas sobre la eficacia del manejo, en particular para los ecosistemas amenazados, para los cuales a menudo hay evidencia limitada y la necesidad de intervenciones inmediatas para garantizar su persistencia. Uno de esos ecosistemas, las turberas alpinas, está amenazado por el cambio climático y otras presiones, proporciona servicios ecosistémicos vitales y sustenta una biodiversidad única. En un taller, recopilamos y sintetizamos en un formato accesible, información de expertos sobre las intervenciones utilizadas, el contexto de la amenaza y la eficacia de la intervención para las turberas alpinas australianas, y utilizamos este conocimiento para evaluar la relevancia local de la literatura mundial para este ecosistema amenazado. Los expertos identificaron 15 intervenciones utilizadas para conservar las turberas australianas, la mayoría de las cuales mejoraron o restauraron la condición de las turberas y abordaron eficazmente diversas amenazas. Las perspectivas de los expertos y los estudios globales se alinearon fuertemente, lo que sugiere que la investigación sobre el manejo de las turberas puede ser ampliamente relevante en otros contextos, a pesar de características distintivas de los sistemas australianos. Nuestro método de de obtención de conocimiento de expertos basado en talleres proporcionó información sobre las prácticas de manejo actuales que no están disponibles en la literatura.

Mental Health Experts as Objects of Epistemic Injustice-The Case of Autism Spectrum Condition.

This theoretical paper addresses the issue of epistemic injustice with particular reference to autism. Injustice is epistemic when harm is performed without adequate reason and is caused by or related to access to knowledge production and processing, e.g., concerning racial or ethnic minorities or patients. The paper argues that both mental health service users and providers can be subject to epistemic injustice. Cognitive diagnostic errors often appear when complex decisions are made in a limited timeframe. In those situations, the socially dominant ways of thinking about mental disorders and half-automated and operationalized diagnostic paradigms imprint on experts' decision-making processes. Recently, analyses have focused on how power operates in the service user-provider relationship. It was observed that cognitive injustice inflicts on patients through the lack of consideration of their first-person perspectives, denial of epistemic authority, and even epistemic subject status, among others. This paper shifts focus toward health professionals as rarely considered objects of epistemic injustice. Epistemic injustice affects mental health providers by harming their access to and use of knowledge in their professional activities, thus affecting the reliability of their diagnostic assessments.

Enhancing global and local decision making for chemical safety assessments through increasing the availability of data.

Toxicity safety assessments are a fundamental part of the lifecycle of products and aim to protect human health and the environment from harmful exposures to chemical substances. To make decisions regarding the suitability of testing strategies, the applicability of individual tests or concluding an assessment for an individual chemical requires data. This review outlines how different forms of data sharing, from enhancing publicly-available data to extracting knowledge from commercially-sensitive data, leads to increased quantity and quality of evidence being available for safety assessors to review. This can result in more confident decisions for different use cases in the context of chemical safety assessments. Although a number of challenges remain with progressing the evolution of toxicity safety assessments, data sharing should be considered as a key approach to accelerating the development and uptake of new best practices.

Using Expert Elicitation for ranking hazards, promoters and animal-based measures for on-farm welfare assessment of indoor reared beef cattle: an Italian experience.

On-farm welfare assessment gives the opportunity to monitor and improve the quality of the animal life on the farm. In order to build the first Italian public standard for the welfare of indoor reared beef cattle, a list of 25 potential hazards and 22 potential promoters of beef cattle welfare was judged by a group of national experts by taking into account their negative or positive impacts on the welfare-state of the target population. In addition, the experts were asked to rank a list of 11 animal-based measures for identifying the most appropriate and important for measuring negative welfare outcomes in beef cattle. Based on experts' ratings, an "impact score" (ISoverall) was calculated for the proposed measures. Management hazards and promoters were ranked to have a greater impact on beef cattle welfare than housing factors. Keeping cattle in large (≥ 40 animals) and heterogeneous groups obtained the highest ISoverall among the proposed hazards (ISoverall = 5.54), followed by the presence of animals without free access to drinking water (ISoverall = 4.39) and the use of high-concentrate corn silage diets (concentrate > 80% and fiber < 6%) (ISoverall = 4.39). On the other hand, housing animals in small (≤ 20 animals) and homogeneous groups (ISoverall = 5.41), checking them at least twice a day (ISoverall = 4.62) and rearing cattle in loose housing systems with access to an outdoor area/pasture (ISoverall = 4.27) were ranked among the top 3 promoters. Concerning animal-based measures, experts scored lameness, severe respiratory diseases, body condition scoring and mortality rate to be measures most important for assessing serious welfare impairment.